full-FORCE: A Target-Based Method for Training Recurrent Networks

Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.Comment: 20 pages, 8 figure

Sumoylation silences the plasma membrane leak K+ channel K2P1.

Reversible, covalent modification with small ubiquitin-related modifier proteins (SUMOs) is known to mediate nuclear import/export and activity of transcription factors. Here, the SUMO pathway is shown to operate at the plasma membrane to control ion channel function. SUMO-conjugating enzyme is seen to be resident in plasma membrane, to assemble with K2P1, and to modify K2P1 lysine 274. K2P1 had not previously shown function despite mRNA expression in heart, brain, and kidney and sequence features like other two-P loop K+ leak (K2P) pores that control activity of excitable cells. Removal of the peptide adduct by SUMO protease reveals K2P1 to be a K+-selective, pH-sensitive, openly rectifying channel regulated by reversible peptide linkage

Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here